Laptop-Price-Prediction

A python interface to predict the price of the laptop according to the specifications of the laptop

Live Project Link : Laptop Price Prediction Live Link

Dataset used : Laptop Price Prediction Kaggle Dataset

Shape of the Dataset : (1302, 11)
Rows: 1302
Columns: 11

Description of Dataset :

Check for Null Values :

There is no Null Value in the Dataset

Description of different Features :

Count Plot for CPU

Count Plot for Touchscreen Feature

Count Plot for Screen Size

Count Plot for Type Name

Count Plot for RAM

Count Plot for OS

Price vs Other Features :

CPU vs Price Plot

RAM vs Price Plot

Laptop Weight(in kg) vs Price Plot

Type Name vs Price Plot

Correlation between Features :

Heatmap for correlation between different features

Features in the final Dataset :

Input Features

• Company
• Type Name
• RAM(GB)
• Weight(kg)
• Touchscreen
• IPS
• PPI
• CPU Brand
• HDD
• SSD
• GPU Brand
• OS

Output Feature

• Price

Final Dataset before encoding

One Hot Encoding :

Code for One Hot Encoding where the number of features increases to 38

Library & Frameworks used :

• Pandas
• Numpy
• Pickle
• Sklearn
• PyCaret
• Streamlit
  
  

Machine Learning Models used :

1. Light Gradient Boosting Machine
2. CatBoost Regressor
3. Random Forest Regressor
4. Gradient Boosting Regressor
5. Extreme Gradient Boosting
6. Ridge Regression
7. Linear Regression
8. K Neighbors Regressor
9. Bayesian Ridge
10. Extra Trees Regressor
11. Huber Regressor
12. AdaBoost Regressor
13. Orthogonal Matching Pursuit
14. Decision Tree Regressor
15. Least Angle Regression
16. Passive Aggressive Regressor
17. Lasso Regression
18. Elastic Net
19. Lasso Least Angle Regression
    
    

Performance of different Machine Learning Models :

Tuning the Hyperparameters of the final ML Model :

Learning Curve for the final Model :

Transformation Pipeline for the final Model :

GUI :

Prediction of Laptop Price :